🔥 Wildfire Damage Prediction
Tech Stack: Python, Pandas, NumPy, Scikit learn, XGBoost, LightGBM, SHAP, Streamlit
Built to raise awareness about fire resistant design, this project uses machine learning to predict wildfire damage with 85 percent accuracy, helping homeowners and policymakers make smarter and safer building decisions.
A machine learning project designed to predict structural damage severity caused by wildfires using CAL FIRE inspection data.
This multi class classification model uses features such as roof construction, exterior siding, and property value to assess risk.
After evaluating multiple models, XGBoost emerged as the top performer. The tool aims to support homeowners and policymakers
in fire resilience planning and includes a live demo for interactive prediction.